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Redondo R.A.F.,IST Austria | Redondo R.A.F.,Francis Crick Institute | De Vladar H.P.,IST Austria | De Vladar H.P.,Center for the Conceptual Foundations of Science | And 4 more authors.
Journal of the Royal Society Interface | Year: 2017

Viral capsids are structurally constrained by interactions among the amino acids (AAs) of their constituent proteins. Therefore, epistasis is expected to evolve among physically interacting sites and to influence the rates of substitution. To study the evolution of epistasis, we focused on the major structural protein of the fX174 phage family by first reconstructing the ancestral protein sequences of 18 species using a Bayesian statistical framework. The inferred ancestral reconstruction differed at eight AAs, for a total of 256 possible ancestral haplotypes. For each ancestral haplotype and the extant species, we estimated, in silico, the distribution of free energies and epistasis of the capsid structure. We found that free energy has not significantly increased but epistasis has. We decomposed epistasis up to fifth order and found that higher-order epistasis sometimes compensates pairwise interactions making the free energy seem additive. The dN/dS ratio is low, suggesting strong purifying selection, and that structure is under stabilizing selection. We synthesized phages carrying ancestral haplotypes of the coat protein gene and measured their fitness experimentally. Our findings indicate that stabilizing mutations can have higher fitness, and that fitness optima do not necessarily coincide with energy minima. © 2017 The Authors.


de Vladar H.P.,Center for the Conceptual Foundations of Science | de Vladar H.P.,Institute of Advanced Studies Koszeg | Santos M.,Autonomous University of Barcelona | Szathmary E.,Center for the Conceptual Foundations of Science | And 3 more authors.
Trends in Ecology and Evolution | Year: 2017

Despite major advances in evolutionary theories, some aspects of evolution remain neglected: whether evolution: would come to a halt without abiotic change; is unbounded and open-ended; or is progressive and something beyond fitness is maximized. Here, we discuss some models of ecology and evolution and argue that ecological change, resulting in Red Queen dynamics, facilitates (but does not ensure) innovation. We distinguish three forms of open-endedness. In weak open-endedness, novel phenotypes can occur indefinitely. Strong open-endedness requires the continual appearance of evolutionary novelties and/or innovations. Ultimate open-endedness entails an indefinite increase in complexity, which requires unlimited heredity. Open-ended innovation needs exaptations that generate novel niches. This can result in new traits and new rules as the dynamics unfolds, suggesting that evolution is not fully algorithmic. Biological evolution appears to be open-ended, but models of evolution have so far failed to account for this phenomenon.Open-endedness minimally requires generating novel phenotypes, but stronger forms entail the continual appearance of evolutionary novelties and innovations.A sensible picture of evolution should describe unfolding of the state space, and not merely its occupancy.State-space unfolding crucially rests on the appearance of exaptations that are neither prestatable nor orderable. Hence, a predictive theory of open-ended evolution might be impossible. © 2017 Elsevier Ltd.


Power D.A.,University of Southampton | Watson R.A.,University of Southampton | Szathmary E.,Center for the Conceptual Foundations of Science | Mills R.,University of Lisbon | And 3 more authors.
Biology Direct | Year: 2015

Background: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? Results: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. Conclusions: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. Reviewers: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder. © 2015 Power et al.


Czaran T.,MTA ELTE Theoretical Biology and Evolutionary Ecology Research Group | Konnyu B.,Eötvös Loránd University | Szathmary E.,MTA ELTE Theoretical Biology and Evolutionary Ecology Research Group | Szathmary E.,Eötvös Loránd University | Szathmary E.,Center for the Conceptual Foundations of Science
Journal of Theoretical Biology | Year: 2015

Metabolically Coupled Replicator Systems (MCRS) are a family of models implementing a simple, physico-chemically and ecologically feasible scenario for the first steps of chemical evolution towards life. Evolution in an abiotically produced RNA-population sets in as soon as any one of the RNA molecules become autocatalytic by engaging in template directed self-replication from activated monomers, and starts increasing exponentially. Competition for the finite external supply of monomers ignites selection favouring RNA molecules with catalytic activity helping self-replication by any possible means. One way of providing such autocatalytic help is to become a replicase ribozyme. An additional way is through increasing monomer supply by contributing to monomer synthesis from external resources, i.e., by evolving metabolic enzyme activity. Retroevolution may build up an increasingly autotrophic, cooperating community of metabolic ribozymes running an increasingly complicated and ever more efficient metabolism.Maintaining such a cooperating community of metabolic replicators raises two serious ecological problems: one is keeping the system coexistent in spite of the different replicabilities of the cooperating replicators; the other is constraining parasitism, i.e., keeping "cheaters" in check. Surface-bound MCRS provide an automatic solution to both problems: coexistence and parasite resistance are the consequences of assuming the local nature of metabolic interactions. In this review we present an overview of results published in previous articles, showing that these effects are, indeed, robust in different MCRS implementations, by considering different environmental setups and realistic chemical details in a few different models. We argue that the MCRS model framework naturally offers a suitable starting point for the future modelling of membrane evolution and extending the theory to cover the emergence of the first protocell in a self-consistent manner. The coevolution of metabolic, genetic and membrane functions is hypothesized to follow the progressive sequestration scenario, the conceptual blueprint for the earliest steps of protocell evolution. © 2015 Elsevier Ltd.


Corning P.A.,Institute for the Study of Complex Systems | Szathmary E.,Center for the Conceptual Foundations of Science | Szathmary E.,Eötvös Loránd University | Szathmary E.,MTA ELTE Theoretical Biology and Evolutionary Ecology Research Group
Journal of Theoretical Biology | Year: 2015

Non-Darwinian theories about the emergence and evolution of complexity date back at least to Lamarck, and include those of Herbert Spencer and the emergent evolution theorists of the later nineteenth and early twentieth centuries. In recent decades, this approach has mostly been espoused by various practitioners in biophysics and complexity theory. However, there is a Darwinian alternative - in essence, an economic theory of complexity - proposing that synergistic effects of various kinds have played an important causal role in the evolution of complexity, especially in the major transitions. This theory is called the synergism hypothesis. We posit that otherwise unattainable functional advantages arising from various cooperative phenomena have been favored over time in a dynamic that the late John Maynard Smith characterized and modeled as synergistic selection. The term highlights the fact that synergistic wholes may become interdependent units of selection. We provide some historical perspective on this issue, as well as a brief explication of the underlying theory and the concept of synergistic selection, and we describe two relevant models. © 2015 Elsevier Ltd.


PubMed | University of Southampton, University of Lausanne, Center for the Conceptual Foundations of Science and University of Lisbon
Type: | Journal: Biology direct | Year: 2015

The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole?Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, unsupervised learning, well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the communitys response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts.This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.


de Vladar H.P.,Center for the Conceptual Foundations of Science | Szathmary E.,Center for the Conceptual Foundations of Science | Szathmary E.,Eötvös Loránd University | Szathmary E.,TMTA ELTE Theoretical Biology and Evolutionary Ecology Research Group
Interface Focus | Year: 2015

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild. © 2015 The Author.


PubMed | Center for the Conceptual Foundations of Science and Eötvös Loránd University
Type: Journal Article | Journal: Interface focus | Year: 2015

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.

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